Minjie Xia

659 citations
9 papers · 392 indexed · h-index 7
Topics
Machine Learning in Healthcare (5 papers)Chronic Disease Management Strategies (2 papers)Pancreatic and Hepatic Oncology Research (1 paper)

In The Last Decade

Minjie Xia

9 papers receiving 383 citations

Peers

Minjie Xia
Comparison fields: 5 of 89
  • Artificial Intelligence 123
  • Health Information Management 84
  • Cardiology and Cardiovascular Medicine 83
  • Epidemiology 79
  • Health Informatics 57
Replace Modi Liu with:
Modi Liu China
Frank Stearns United States
Shaun T Alfreds United States
Le Zheng China
Jennifer H. Garvin United States
Gina Barnes United States
Oliver Wang United States
Anurag Garikipati United States
Andoni Beristain Spain
Megan E. Salwei United States
Minjie Xia relative to Modi Liu China Modi Liu's profile →
Citations per field
00.5×1.5×
Modi Liu · 1×
Citations per year

Countries citing papers authored by Minjie Xia

Since Specialization
Citations

This map shows the geographic impact of Minjie Xia's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Minjie Xia with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Minjie Xia more than expected).

Fields of papers citing papers by Minjie Xia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Minjie Xia. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Minjie Xia. The network helps show where Minjie Xia may publish in the future.

Co-authorship network of co-authors of Minjie Xia

This figure shows the co-authorship network connecting the top 25 collaborators of Minjie Xia. A scholar is included among the top collaborators of Minjie Xia based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Minjie Xia. Minjie Xia is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

9 of 9 papers shown
#WorkIndexed citations
1 10
2 7
3
A Deep-Learning Based Prediction of Pancreatic Adenocarcinoma with Electronic Health Records from the State of Maine
4
4 64
5 58
6 35
7 38
8 171
9 5

About Minjie Xia

Minjie Xia is a scholar working on Geriatrics and Gerontology, Physical Therapy, Sports Therapy and Rehabilitation and Health Information Management, having authored 9 papers that have together received 392 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (5 papers), Chronic Disease Management Strategies (2 papers) and Pancreatic and Hepatic Oncology Research (1 paper). The work is most often cited by research in Health Informatics (57 citations), Health Information Management (84 citations) and Physical Therapy, Sports Therapy and Rehabilitation (29 citations). Minjie Xia has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Shiying Hao, Modi Liu, Xuefeng B. Ling, Doff B. McElhinney, Eric Widen, Karl G. Sylvester, Chengyin Ye, Frank Stearns, Bo Jin and Oliver Wang. Their work appears in journals such as Environmental Science & Technology, PLoS ONE and Journal of Medical Internet Research.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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